# Required packages
import pandas as pd
import numpy as np
import geopandas as gpd
import json
import os
import pickle

## Visualisation libraries

# Maps
import geoplot as gplt
import geoplot.crs as gcrs
import mplleaflet
from shapely.geometry import Point, Polygon
import folium
from folium import plugins
from folium.plugins import HeatMap

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## progressbar
import progressbar

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Real Estate

Calgary Property Assessments

• Preprocessing
• Exploratory Data Analysis

Calgary Property Assessments

The assessed values of residential, non-residential, and farmland properties in Calgary. The properties in this dataset consist of Calgary lands that have a registered parcel at Alberta’s Land Titles Office. Properties that are on The City of Calgary’s annual property assessment rolls, but excluded from this dataset, are titled parking stalls, titled storage units, machinery & equipment property, and linear property (as defined in the Municipal Government Act). This dataset can be downloaded from here.

For detailed property assessment information, visit https://assessmentsearch.calgary.ca

Table of contents

• Property Assessments
• Datasets
• Data Analysis
  • Average Value (the Current Year))
    • Average Value (Non-residential))
    • Average Value (Residential))
    • Average Value (Farmland))
    • Average Value (Machinery And Equipment))
    • Assessed Average Value by Sector
    • Assessed Average Value by Community Structure
  • Maps
    • Assessed Average Value by Community
    • Asset Values in Each Community
• Predictions

Path = 'Calgary/Property_Assessments.csv'

def Header(Text, L = 100, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
    
def Line(L=100, C = 'Blue'):
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)

def Search_List(Key, List): return [s for s in List if Key in s]

Data = pd.read_csv(Path.split(".")[0]+'_Mod.csv')
Header('Calgary Property Assessments:')
display(Data.head(4).style.hide_index())
display(pd.DataFrame({'Number of Instances': [Data.shape[0]], 'Number of Attributes': [Data.shape[1]]}).style.hide_index())

# Dictionaries
with open(Path.split(".")[0] + '_Feat_Dict.pkl', 'rb') as fp:
    Feat_Dict = pickle.load(fp)
    
display(pd.DataFrame({'Feature': Feat_Dict.keys(), 'Description': Feat_Dict.values()}).style.hide_index())

Calgary Property Assessments: ======================================================================

Roll_Year	Roll_Number	Address	Assessed_Value	Assessment_Class	Assessment_Class_Description	RE_Assessed_Value	NR_Assessed_Value	FL_Assessed_Value	Community_Code	Community_Name	Latitude	Longitude	Unique_Key	Year_Of_Construction	Land_Use_Designation	Property_Type	Land_Size_SM	Land_Size_SF	Land_Size_AC	Sub_Property_Use	Point	Community_Structure	Sector	Class_Code	Srg	Class
2019	202658902	215 4350 SETON DR SE	220000.000000	RE	Residential	220000.000000	nan	nan	SET	Seton	50.877365	-113.947337	2019202658902	nan	nan	nan	nan	nan	nan	nan	POINT (-113.947336601944 50.877364528547)	Building Out	Southeast	1	Developing	Residential
2020	202682290	29V 230 SETON PS SE	10000.000000	RE	Residential	10000.000000	nan	nan	SET	Seton	50.879463	-113.947337	2020202682290	nan	M-1	LO	14.000000	151.000000	0.000000	TPU	nan	Building Out	Southeast	1	Developing	Residential
2019	202681714	192V 19621 40 ST SE	10000.000000	RE	Residential	10000.000000	nan	nan	SET	Seton	50.879463	-113.947337	2019202681714	nan	nan	nan	nan	nan	nan	nan	nan	Building Out	Southeast	1	Developing	Residential
2019	202681938	214V 19621 40 ST SE	10000.000000	RE	Residential	10000.000000	nan	nan	SET	Seton	50.879463	-113.947337	2019202681938	nan	nan	nan	nan	nan	nan	nan	nan	Building Out	Southeast	1	Developing	Residential

Number of Instances	Number of Attributes
7849557	27

Feature	Description
Roll_Year	Assessment Roll Year
Roll_Number	Assessment Account Roll Number
Address	Civic Address
Assessed_Value	Total Assessed Value of the Property
Assessment_Class	Predominant Assessment Class Code
Assessment_Class_Description	Description of Predominant Assessment Class
RE_Assessed_Value	Assessed Value of Residential Property
NR_Assessed_Value	Assessed Value of Non-Residential Property
FL_Assessed_Value	Assessed Value Of Farmland
Community_Code	Community Code
Community_Name	Community Name
Latitude	Latitude
Longitude	Longitude
Unique_Key	Unique Key
Year_Of_Construction	Account AYOC (Actual Year of Construction)
Land_Use_Designation	Land Use Designation
Property_Type	Account Property Type
Land_Size_SM	Account Assessable Land Area Square Meters
Land_Size_SF	Account Assessable Land Area Square Feet
Land_Size_AC	Account Assessable Land Area Square Acres
Sub_Property_Use	Account Sub Property Type
Point	Point

We can also use the community boundaries for the city of Calgary from here.

Community_Boundaries = gpd.read_file('Calgary/Community_Boundaries.geojson')
# Chaning the headers to title
mylist = Community_Boundaries.columns.tolist()
mylist = [x.replace('comm_','Community_').title() for x in mylist]
mylist = [x.replace('Geometry','geometry') for x in mylist]
Community_Boundaries.columns = mylist
del mylist
display(Community_Boundaries.head(4))

	Community_Structure	Name	Sector	Class_Code	Srg	Class	Community_Code	geometry
0	INNER CITY	SUNALTA	CENTRE	1	BUILT-OUT	Residential	SNA	POLYGON ((-114.10061 51.04799, -114.10056 51.0...
1	BUILDING OUT	WEST SPRINGS	WEST	1	DEVELOPING	Residential	WSP	POLYGON ((-114.18781 51.06151, -114.18781 51.0...
2	OTHER	12A	SOUTHEAST	4	N/A	Residual Sub Area	12A	POLYGON ((-113.86945 50.97957, -113.86945 50.9...
3	1950s	WINDSOR PARK	CENTRE	1	BUILT-OUT	Residential	WND	POLYGON ((-114.08152 51.00872, -114.08137 51.0...

Exploratory Data Analysis

First, of all the dataset includes the following assessment roll years.

Years = np.sort(Data.Roll_Year.unique())

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 0.25), sharex=False)
_ = sns.boxplot(x=Years, ax=ax, orient='h', color='#2ecc71')
_ = ax.set_xlabel('Years')
_ = ax.set_xlim([Years.min(), Years.max()])
_ = ax.set_xticks(Years)
_ = ax.grid('off')

In particular, in each year:

def FeatDispPlot2(Feat, Title, LC = 'Black',
                  Colors =  ['HoneyDew','GreenYellow', 'Bisque','LightSalmon','Plum', 'LightSkyBlue'],
                  yLim = [0, 9e3], H = 450, titleY = 0.92, Inp = Data):
    Group = Inp.groupby([Feat])[Feat].agg({'count'}).rename(columns = {'count':'Count'})
    Group['Percentage'] = np.round(100* Group.values /Group.sum().values, 2)
    Group.reset_index(drop = False, inplace = True)
    
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.05,
                        column_widths=[0.6, 0.4],specs=[[{"type": "xy"}, {'type':'domain'}]])
    # Left
    fig.add_trace(go.Bar(x= Group[Feat].values, y= Group['Count'].values,
                         marker_color= Colors, text = 'Count',
                         textposition='inside', showlegend = False, hovertext=list(Group['Percentage'])), 1, 1)
    fig.update_layout(plot_bgcolor= 'white')
    fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1,
                      texttemplate= 10*' ' + '%{value}', textposition='inside', row=1, col=1)
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                 showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range= yLim)
    # Right
    fig.add_trace(go.Pie(labels= Group[Feat].values, values= Group['Count'].values, textfont=dict(size=16),
                     marker=dict(colors = Colors, line=dict(color='black', width=1))), 1, 2)
    fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1, row=1, col=2)
    fig.update_layout(plot_bgcolor= 'white', width = 900,
                  title={'text': '<b>' + Title + '<b>',
                         'x':0.5, 'y':0.90, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()

def Roll_Number_Data (rn):
    Out = Data[Data.Roll_Number == rn].sort_values(by='Roll_Year', ascending=True).reset_index(drop =True)
    Cols = Out.isna().sum()
    Cols = Cols.loc[(Cols>0) & (Cols< Cols.max())]
    Cols = Cols.index
    Temp = Out[Cols]
    Out[Cols] = Temp.fillna(value=dict(zip(Temp.columns, Temp.dropna().values[0])))
    return Out

def Roll_Number_Table(Inp):
    Replace_Dict = {'Roll_Year':'Year','Assessment_Class_Description':'Class', '_':' ', 'Of':'of',
                   'Srg':'SRG'}
    Temp = Inp.columns
    for key in list(Replace_Dict.keys()):
        Temp = [x.replace(key,Replace_Dict[key]) for x in Temp]
    Inp.columns = Temp

    Featured_Cols = ['Year','Address', 'Assessed Value','Land Size SF', 'Class', 'Community Name', 'Year of Construction','Community Structure', 'Sector', 'SRG']
    Inp = Inp[Featured_Cols]
    try:
        Inp[['Year of Construction','Land Size SF']] = Inp[['Year of Construction','Land Size SF']].astype('int32')
    except:
        pass
    Inp = Inp.reset_index(drop = True)
    display(Inp.style.hide_index().set_precision(0).bar(subset=['Assessed Value'], align='left', color=['Lime'])\
            .set_properties(subset=['Year'], **{'background-color': 'Indigo', 'color': 'White'}))

def address_map(Inp, Zoom=12.5, Icon='info-sign'):
    m = folium.Map(location=[Inp.Latitude[0], Inp.Longitude[0]], zoom_start=Zoom)
    folium.Marker(location=[Inp.Latitude[0], Inp.Longitude[0]],popup = Inp.Address[0],
                  icon=folium.Icon(icon=Icon)).add_to(m)
    display(m)
    return

def Unit_Price(Inp):
    fig, ax = plt.subplots(figsize=(14,5))
    fig = sns.regplot(x='Roll_Year', y='Assessed_Value', data=Inp, color='#e74c3c',
                      scatter_kws={'s': 50, 'color': '#34495e'},order=10, ci=None, truncate=False)
    _ = ax.set_title('The Price Fluctuations for %s Unit at %s (%s)' % (Inp.Assessment_Class_Description.tolist()[-1],
                                                                        Inp.Address.tolist()[-1],
                                                                       Inp.Comm_Name.unique()[0]))
    _ = ax.set_ylabel('Assessed')
    _ = ax.set_xlabel('Price')
    _ = ax.set_xticks(np.arange(Inp.Roll_Year.min(),Inp.Roll_Year.max()+1,1)) 
    
def Community_Ave_yr(Yr = 2021, Sort = True, Inp = Data, Boundaries = Community_Boundaries):
    Temp = Inp[Inp['Roll_Year'] == Yr]
    Cols = ['Community_Code', 'Community_Name', 'Assessment_Class_Description', 'Community_Structure', 'Sector']
    Group = Temp.groupby(Cols)['Assessed_Value'].agg({'mean','median', 'max', 'min'})
    Group = Group.rename(columns = {'mean':'Average Value', 'median':'Median Value', 'max':'Maximum Value', 'min':'Minimum Value'})
    Group = Group.reset_index(drop = False)
    Out = Group.merge(Boundaries [['Community_Code', 'geometry']], how='right', on='Community_Code')
    Out = Out[pd.notnull(Out['Average Value'])]
    Out.columns = [x.replace('_',' ') for x in Out.columns]
    if Sort:
        Out = Out.sort_values(by='Average Value', ascending=False)
    return Out 
    
FeatDispPlot2('Roll_Year', Title = 'The Number of Rolling by Year', yLim = [0, 6e5],
              Colors = px.colors.qualitative.Light24)

Consider the assessed values only in the current year:

Current_year = Data[Data['Roll_Year']==Data['Roll_Year'].max()].sort_values(by = ['Assessed_Value'], ascending= False)

For the most expensive unit we have,

rn = Current_year['Roll_Number'].values[0]
Table = Roll_Number_Data(rn).drop(columns = ['Class'])
Roll_Number_Table(Table)
address_map(Table, 15.5)
print('As can be seen the unit is a %s unit, and it is located in %s.' % (Table['Class'].values[-1],
                                                                          Table['Community Name'].values[-1])) 

Year	Address	Assessed Value	Land Size SF	Class	Community Name	Year of Construction	Community Structure	Sector	SRG
2010	4448 FRONT ST SE	3500000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2011	4448 FRONT ST SE	5610000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2012	4448 FRONT ST SE	6550000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2013	4448 FRONT ST SE	6550000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2014	4448 FRONT ST SE	745840000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2015	4448 FRONT ST SE	933320000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2016	4448 FRONT ST SE	930080000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2017	4448 FRONT ST SE	932400000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2018	4448 FRONT ST SE	1449330000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2019	4448 FRONT ST SE	1482170000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2020	4448 FRONT ST SE	1492130000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing
2021	4448 FRONT ST SE	1498350000	1427341	Non-residential	Seton	2007	Building Out	Southeast	Developing

Make this Notebook Trusted to load map: File -> Trust Notebook

As can be seen the unit is a Non-residential unit, and it is located in Seton.

Average Value (the Current Year)

The following function can be used for calculating the Average Value of each community.

Community_Ave = Community_Ave_yr(Sort = True)
Community_Ave.iloc[:,:-1].head(15).style.format({'Maximum Value': "{:.2e}", 'Minimum Value': "{:.2e}",
                                                 'Median Value': "{:.2e}", 'Average Value': "{:.2e}"}).hide_index().\
                                                  background_gradient(subset= ['Average Value'], cmap='GnBu')

Community Code	Community Name	Assessment Class Description	Community Structure	Sector	Median Value	Maximum Value	Minimum Value	Average Value
DNW	Downtown West End	Machinery And Equipment	Centre City	Centre	3.66e+08	3.66e+08	3.66e+08	3.66e+08
EAG	Eagle Ridge	Non-residential	1960S/1970S	South	1.28e+05	4.18e+08	8.75e+04	1.39e+08
MEA	Meadowlark Park	Non-residential	1950S	Centre	3.54e+05	1.14e+09	2.70e+03	9.52e+07
SET	Seton	Non-residential	Building Out	Southeast	3.31e+06	1.50e+09	2.15e+04	5.23e+07
UOC	University Of Calgary	Non-residential	Other	Northwest	8.19e+05	7.22e+08	1.62e+04	4.80e+07
12B	Residual Ward 12 - Sub Area 12B	Non-residential	Undeveloped	Southeast	4.96e+05	8.00e+08	0.00e+00	4.40e+07
COP	Canada Olympic Park	Non-residential	Other	West	4.02e+06	1.27e+08	9.43e+05	4.38e+07
NPK	Nose Hill Park	Non-residential	Parks	Northwest	5.02e+06	2.09e+08	3.70e+04	3.44e+07
EAU	Eau Claire	Non-residential	Centre City	Centre	1.84e+06	4.51e+08	0.00e+00	3.34e+07
09P	Residual Ward 9 - Sub Area 9P	Non-residential	Undeveloped	East	4.59e+06	1.45e+08	3.74e+03	3.32e+07
HSN	Haskayne	Non-residential	Building Out	Northwest	3.50e+04	3.41e+08	0.00e+00	3.18e+07
UOC	University Of Calgary	Residential	Other	Northwest	2.95e+07	4.32e+07	1.36e+07	2.93e+07
02E	Residual Ward 2 - Sub Area 2E	Non-residential	Other	Northwest	6.43e+06	1.49e+08	9.35e+05	2.73e+07
QPK	Queens Park Village	Residential	1950S	Centre	2.66e+07	2.66e+07	2.66e+07	2.66e+07
LPK	Lincoln Park	Non-residential	1950S	West	3.84e+06	3.13e+08	2.90e+04	2.24e+07

def AvgValueClass_Plot(Class, H = 600, Top = 40, yt = None, Community_Ave = Community_Ave, Horizontal = True,
                  marker_color='rgba(58, 71, 80, 0.6)', marker_line_color='rgba(58, 71, 80, 1.0)'):
    
    Group = Community_Ave[Community_Ave['Assessment Class Description'] == Class][['Community Name','Average Value']]
    if not Top == None:
        if Group.shape[0]>Top:
            Group = Group.head(40)        
    if yt == None:
        yt = Group['Average Value'].max()
        n = (len(str(int(yt))) -1)
        yt = int(np.ceil(yt/(10**n))*(10**n))
    if Horizontal:
        fig = px.bar(Group, x='Community Name', y='Average Value')
        fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
        fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= [0, yt])
    else:
        fig = px.bar(Group, y='Community Name', x='Average Value')
        fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=False, gridwidth=1, gridcolor='Lightgray')
        fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                     zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                     showgrid=True, gridwidth=1, gridcolor='Lightgray', range= [0, yt])
        
    fig.update_traces(marker_color=marker_color, marker_line_color=marker_line_color,
                      marker_line_width=1.5, opacity=1)
    fig.update_layout(plot_bgcolor= 'white', height= H, width = 980,
                      title={'text': '<b>' + 'Average Value (%s)' % Class + '<b>',
                             'x':0.5, 'y':0.95, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()

def AvgValue_Plot(Feat, H = 600, Top = 40, yt = None, Community_Ave = Community_Ave,
                  marker_color='rgba(58, 71, 80, 0.6)', marker_line_color='rgba(58, 71, 80, 1.0)'):
    
    Group = pd.DataFrame(Community_Ave.groupby([Feat])['Average Value'].mean()).reset_index()
    if not Top == None:
        if Group.shape[0]>Top:
            Group = Group.head(40)        
    if yt == None:
        yt = Group['Average Value'].max()
        n = (len(str(int(yt))) -1)
        yt = int(np.ceil(yt/(10**n))*(10**n))
    
    fig = px.bar(Group, x='Average Value', y= Feat)
    fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 zeroline=False, zerolinewidth=1, zerolinecolor='Black',
                 showgrid=False, gridwidth=1, gridcolor='Lightgray')
    fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 zeroline=True, zerolinewidth=1, zerolinecolor='Black',
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range= [0, yt])   
    fig.update_traces(marker_color=marker_color, marker_line_color=marker_line_color,
                      marker_line_width=1.5, opacity=1)
    fig.update_layout(plot_bgcolor= 'white', height= H, width = 980,
                      title={'text': '<b>' + 'Average Value (%s)' % Feat + '<b>',
                             'x':0.5, 'y':0.95, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()

Average Value (Non-residential)

AvgValueClass_Plot(Class = 'Non-residential', Top = 50, yt = 160e6)

Average Value (Residential)

AvgValueClass_Plot(Class = 'Residential', marker_color='rgb(158,202,225)', marker_line_color='rgb(8,48,107)')

Average Value (Farmland)

AvgValueClass_Plot(Class = 'Farm land', marker_color='rgb(173,255,47)', marker_line_color='rgb(34,139,34)')

Average Value (Machinery And Equipment)

AvgValueClass_Plot(Class = 'Machinery And Equipment', marker_color='rgb(147,112,219)', marker_line_color='rgb(75,0,130)',
              H = 180, Horizontal = False)

Assessed Average Value by Sector

AvgValue_Plot(Feat = 'Sector', H = 400, marker_color='MistyRose', marker_line_color='DarkRed')

Assessed Average Value by Community Structure

AvgValue_Plot(Feat = 'Community Structure', H = 550, marker_color='CornSilk', marker_line_color='DarkOrange', yt = 35e6)

Maps

Assessed Average Value by Community

def Community_Ave_yr(Yr, Inp=Data):
    Temp = Data[Data['Roll_Year']==Yr]
    Out = pd.DataFrame(Temp.groupby('Community_Code')['Assessed_Value'].mean())
    Out.columns =['Average Value']
    Out = Out.reset_index()
    del Temp
    Temp = pd.merge(Community_Boundaries, Out, how ='inner', on ='Community_Code')
    Out = Temp.copy()
    del Temp
    Out = Out[['Average Value', 'geometry']]
    return Out 

proj = gcrs.AlbersEqualArea(central_latitude = Data.Latitude.mean(), central_longitude = Data.Longitude.mean())
f, ax = plt.subplots(1, 2, figsize=(18, 12), subplot_kw={'projection': proj})

# Left Plot
Temp = Community_Ave_yr(Data.Roll_Year.max())
_ = gplt.choropleth(Temp, hue = 'Average Value', cmap='Blues', linewidth=0.0, ax = ax[0], legend=True,
                    legend_kwargs={'shrink': .5, "aspect":30})
_ = gplt.polyplot(Temp, edgecolor='black', linewidth=0.8, ax = ax[0])
_ = ax[0].set_title('Assessed Average Value by Community in %i'% Data.Roll_Year.max())
del Temp

# Right Plot
Temp = Community_Ave_yr(Data.Roll_Year.min())
_ = gplt.choropleth(Temp, hue = 'Average Value', cmap='Reds', linewidth=0.0, ax = ax[1], legend=True,
                    legend_kwargs={'shrink': .5, "aspect":30})
_ = gplt.polyplot(Temp, edgecolor='black', linewidth=0.6, ax = ax[1])
_ = ax[1].set_title('Assessed Average Value by Community in %i' % Data.Roll_Year.min())
del Temp 

Asset Values in Each Community

We can define the following heatmap to show the Assessed values in each community. In each case, the to fifty most values asses are also marked on the map.

def map_points(df, Zoom = 13, TOP = 10, heat_map_weights_col='Assessed_Value', Normalize=False, heat_map_radius=10):
    
    M = folium.Map(location=[df['Latitude'].median(), df['Longitude'].median()], zoom_start = Zoom)
    if heat_map_weights_col is None:
        Cols = ['Latitude', 'Longitude']
    else:
        if Normalize:
            df[heat_map_weights_col] = df[heat_map_weights_col] / df[heat_map_weights_col].sum()
        Cols = ['Latitude', 'Longitude', heat_map_weights_col]
    
    M.add_child(plugins.HeatMap(df[Cols].values, radius=heat_map_radius))
    
    Tops = df.head(TOP)
    for i in range(len(Tops)):
        Inp=df[i:(i+1)]
        St = '(Value:' + str(Inp.Assessed_Value.values[0])+', Class:' + Inp.Assessment_Class_Description.values[0] + ')'
        if (Inp.Assessment_Class.values[0] == 'RE'):
            folium.Marker(location=[Inp.Latitude.values[0], Inp.Longitude.values[0]], popup = St,
                          icon=folium.Icon(color='purple',icon='building', prefix='fa') ).add_to(M)
        else:
            folium.Marker(location=[Inp.Latitude.values[0], Inp.Longitude.values[0]], popup = St).add_to(M)
            
    display(M)

For example, consider the Seton community.

Group = Data[Data.Roll_Year == Data.Roll_Year.max()]
Group = Group[Group['Community_Name'] == 'Seton']
map_points(Group, 14, 50)
del Group

Make this Notebook Trusted to load map: File -> Trust Notebook

Similarly, for the Brentwood community, we have,

Group = Data[Data.Roll_Year == Data.Roll_Year.max()]
Group = Group[Group['Community_Name'] == 'Brentwood']
map_points(Group, 14, 50)
del Group

Make this Notebook Trusted to load map: File -> Trust Notebook

Predictions

Unfortunately, the given data are not suitable for any forms of predictions since we mainly know the location of an asset, and their classes and values. Hopefully, in the future, this study can be extended.

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References

1. Property Assessments Dataset

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